PSAT241 Variation in the Diagnosis of Noninvasive Follicular Thyroid Neoplasm With Papillary-Like Nuclear Features (NIFTP) in the United States
Bibliographic record
Abstract
Abstract Background The North American Association of Central Cancer Registries (NAACCR) develops and promotes uniform data standards for cancer registries, such as uniform cancer coding, and is used by all central cancer registries in the United States (US) and Canada, including Surveillance, Epidemiology and End Results (SEER). Effective January 1, 2017, the NAACCR modified its coding scheme and noninvasive encapsulated follicular variant of papillary thyroid cancer (EFVPTC) was reclassified as non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) to reflect the indolent nature and very low risk of adverse outcomes of this thyroid tumor. The diagnostic use of NIFTP was anticipated to impact tens of thousands of patients in the US. Since NIFTP is no longer considered a cancer, as of January 1, 2021, it was no longer a reportable diagnosis in SEER. However, little is known about how the diagnosis of NIFTP was utilized across different regions and patient populations in the US when it was a reportable diagnosis. Methods Data was extracted from the US SEER-21 cancer registry (2000-2018). The study cohort comprised of individuals diagnosed with papillary or follicular thyroid cancer (2000-2018), or NIFTP (2017-2018). We examined the annual incidence of thyroid cancer by subtypes and NIFTP. Using data for 2018, we determined the rates of NIFTP for each of the 16 sites included in SEER-21. In addition, we compared the demographics of patients diagnosed with NIFTP to that of patients diagnosed with papillary and follicular thyroid cancer using Chi-square test. Results Between 2010 and 2018, we identified a total of 191,107 cases (182,893 PTC, 7,445 FTC, and 769 NIFTP). Incidence of FVPTC sharply declined from 2015 to 2018, with observed increases in NIFTP and encapsulated PTC/ invasive EFVPTC each accounting for 17% and 10% of the decline in FVPTC, respectively. High heterogeneity was observed in the regional incidence of NIFTP in 2018, with incidence rates ranging from 0.0% (Alaska) to 5.8% (Seattle-Puget Sound). Based on 2018 data, a diagnosis of NIFTP (2.2% of total thyroid cancer cases) was significantly associated with female sex (P=0.001), Black race (P<0.001), and non-Hispanic ethnicity (P<0.001) compared to diagnosis of papillary and follicular thyroid cancer. Conclusion There is marked variation in the use of the NIFTP diagnoses. The recent NAACCR coding change that resulted in NIFTP, a tumor with uncertain malignant potential and for which there is no long-term outcome data available, no longer being a reportable diagnosis will disproportionately affect women and Black patients, and patients who reside in regions with higher rates of NIFTP diagnoses. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".